Product processing and min processing are the predominant CBF-based methods for coprime and nested arrays [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}. Various aspects of these two processors are compared in depth in [7]}, [9]}, [13]}, with product processing having the advantage of being less vulnerable to crossterms than min processing [7]}, [9]}, [16]}. Other methods that have been popular for coprime and nested arrays are subspace-based algorithms such as MUSIC and ESPRIT [1]}, [2]}, [19]}, [20]}, [21]}, [22]}, [23]}, [24]}, [25]}, [26]}, [27]}. For lattice-imposed planar arrays, these methods exhibit very low resolution when compared with a PPO due a narrow filled coarray [28]}. Because these algorithms process an estimate of the signal correlation matrix to obtain an approximate DoA, the statistical properties of the PPO also provide a means to analyze subspace-based algorithms.
